Kurzbeschreibung (Abstract)

We present a novel parametric representation of general dynamic driving environments. It is particularly suitable for near-future Advanced Driver Assistance Systems due to its compactness, inherent consistency between static and dynamic entities, suppression of irrelevant details, as well as its sensor-independent, real-time capable generation. By building upon a common occupancy grid map-based environment representation, cells belonging to dynamic objects are simultaneously extracted, classified, and tracked in an object-based manner via an Interacting-MultipleModel-Unscented-Kalman-Probabilistic-Data-Association (IMM-UK-PDA) filter and cleared from the grid. The remaining static environment grid is subsequently processed by methods from the image analysis domain, followed by an Information Filter-based contour tracking of relevant free space boundaries to create a spatiotemporally smooth compact Parametric Free Space (PFS) map. The PFS map represents the static part of the traffic scene with modest bandwidth requirements. The system runs in real time on an experimental vehicle equipped with an automotive radar and a stereo camera and is evaluated within real traffic environments.

We present a novel parametric representation of general dynamic driving environments. It is particularly suitable for near-future Advanced Driver Assistance Systems due to its compactness, inherent consistency between static and dynamic entities, suppression of irrelevant details, as well as its sensor-independent, real-time capable generation. By building upon a common occupancy grid map-based environment representation, cells belonging to dynamic objects are simultaneously extracted, classified, and tracked in an object-based manner via an Interacting-MultipleModel-Unscented-Kalman-Probabilistic-Data-Association (IMM-UK-PDA) filter and cleared from the grid. The remaining static environment grid is subsequently processed by methods from the image analysis domain, followed by an Information Filter-based contour tracking of relevant free space boundaries to create a spatiotemporally smooth compact Parametric Free Space (PFS) map. The PFS map represents the static part of the traffic scene with modest bandwidth requirements. The system runs in real time on an experimental vehicle equipped with an automotive radar and a stereo camera and is evaluated within real traffic environments.